optopsy 2.0.0b2

Python Backtesting library for options trading strategies

Optopsy

Optopsy is a nimble backtesting and statistics library for option strategies, it is designed to answer questions like "How do vertical spreads perform on the SPX?" or "Which strikes and/or expiration dates should I choose to make the most potential profit?"

This library is currently being rewritten and is in beta state, use at your own risk

Supported Option Strategies

• Calls/Puts
• Straddles/Strangles (WIP)
• Butterflies/Iron Condors (WIP)
• Many more to follow

Documentation

Please see the wiki for API reference.

Methodology

Most backtesting tools for option strategies don't reveal how the options are backtested, since this is open sourced, I will try to be as transparent as possible on how the algorithm works. You can decide if it works for your situation. My aim for this libary is not to be able to backtest every possible event driven scenarios out there, but to focus on answering the core questions laid out in the introduction. There are no fancy features such as being 'event driven', but theoretically, it is possible by injecting a list of entry and exit dates created from external sources based on indicators.

There are two main parts of the library:

• Statistics Module - Focused on generating statistics on various options strategies (Independent of chronological events)
• Backtester Module (Coming Soon) - Replay the generated statistics with chronological order to create an approximated backtest.

The algorithm for the statistics module is as follows:

1. Evaluate all option chains provided (filter for desired the entry and exit dates and calculate the profit/loss of each individual option chain)
2. Group each 'evaluated' option chain into buckets (buckets are assigned to 'Days to Expiration' (grouped by days in intervals of 7, by default), and either 'delta' or 'strike distance percent' from current price (grouped in intervals of 5%, by default). Obviously, the smaller the intervals, the more accurate the results should be
3. Construct the legs of the option strategy with the previously evaluated amounts and net out the profit/loss
4. Aggregate all the constructed strategies into their buckets and calculate the average profit loss for each bucket combination
5. The result will contain the average profit/loss amounts of the strategy (and other statistics such as min/max, distributions) for all the combinations of inputs (strike dist %/ DTE)

Obviously strategy statistics do not take into account real world events in a chronological order. For that, there is the backtestercc module that will replay each backtest ordered by expiration dates with a running balance to simulate what would have happened.

Notes

As the algorithm is heavily based on bucketing and approximations to improve performance, it is not recommeded to make trade decisions based on the results solely from this library. Please use at your own risk.

Usage

Use Your Data

• Use data from any source, just provide a Pandas dataframe with the required columns when calling optopsy functions.

Dependencies

You will need Python 3.6 or newer and Pandas 0.23.1 or newer and Numpy 1.14.3 or newer.

Installation

pip install optopsy==2.0.0b1

Example

Let's see how long calls perform on the SPX on a small demo dataset on the SPX: Download the following data sample from DeltaNeutral: http://www.deltaneutral.com/files/Sample_SPX_20151001_to_20151030.csv

This dataset is for the month of October in 2015, lets load it into Optopsy. First create a small helper function that returns a file path to our file. We will store it under a folder named 'data', in the same directory as the working python file.

def filepath():
    curr_file = os.path.abspath(os.path.dirname(__file__))
    return os.path.join(curr_file, "./data/Sample_SPX_20151001_to_20151030.csv")

Next lets use this function to pass in the file path string into Optopsy's csv_data() function, we will map the column indices using the defined function parameters. We are omitting the start_date and end_date parameters in this call because we want to include the entire dataset. The numeric values represent the column number as found in the sample file, the numbers are 0-indexed:

import optopsy as op

spx_data = op.csv_data(
    filepath(),
    underlying_symbol=0,
    underlying_price=1,
    option_type=5,
    expiration=6,
    quote_date=7,
    strike=8,
    bid=10,
    ask=11,
)

The csv_data() function is a convenience function. Under the hood it uses Panda's read_csv() function to do the import. There are other parameters that can help with loading the csv data, consult the code/future documentation to see how to use them.

Optopsy is a small simple library that offloads the heavy work of backtesting option strategies, the API is designed to be simple and easy to implement into your regular Panda's data analysis workflow. As such, we just need to call the single_calls() function to have Optopsy generate all combinations of a simple long call strategy for the specified time period and return a DataFrame. Here we also use Panda's round() function to return data within two decimal places.

long_single_calls = op.singles_calls(spx_data, strike_dist_pct_interval=0.05, side="long").round(2)

The function will returned a Pandas DataFrame containing the statistics of running long calls in all valid combinations on the SPX (The example below is truncated):

	dte_range	strike_otm_pct_range	long_profit_pct_min	long_profit_pct_max	long_profit_pct_mean	long_profit_pct_median	long_profit_pct_std	long_profit_pct_count
0	(0, 7]	(-1.0, -0.95]	-0.01	0.07	0.02	0.02	0.02	20
1	(0, 7]	(-0.95, -0.9]	-0.01	0.06	0.02	0.02	0.02	21
2	(0, 7]	(-0.9, -0.85]	0	0.07	0.03	0.02	0.02	20
3	(0, 7]	(-0.85, -0.8]	-0.01	0.06	0.02	0.02	0.02	27
4	(0, 7]	(-0.8, -0.75]	0	0.08	0.03	0.02	0.02	27
5	(0, 7]	(-0.75, -0.7]	-0.01	0.08	0.03	0.02	0.02	33
6	(0, 7]	(-0.7, -0.65]	-0.01	0.09	0.03	0.02	0.03	40
7	(0, 7]	(-0.65, -0.6]	-0.01	0.09	0.03	0.02	0.03	37
8	(0, 7]	(-0.6, -0.55]	-0.02	0.09	0.03	0.02	0.03	73
9	(0, 7]	(-0.55, -0.5]	-0.02	0.1	0.03	0.03	0.03	158
10	(0, 7]	(-0.5, -0.45]	-0.02	0.11	0.03	0.03	0.03	184
11	(0, 7]	(-0.45, -0.4]	-0.02	0.12	0.04	0.03	0.03	201
12	(0, 7]	(-0.4, -0.35]	-0.02	0.13	0.04	0.04	0.04	247
13	(0, 7]	(-0.35, -0.3]	-0.02	0.15	0.05	0.04	0.04	296
14	(0, 7]	(-0.3, -0.25]	-0.03	0.17	0.05	0.05	0.05	329
15	(0, 7]	(-0.25, -0.2]	-0.03	0.2	0.06	0.05	0.05	352
16	(0, 7]	(-0.2, -0.15]	-0.04	0.26	0.08	0.07	0.07	383
17	(0, 7]	(-0.15, -0.1]	-0.06	0.37	0.11	0.09	0.09	417
18	(0, 7]	(-0.1, -0.05]	-0.12	0.69	0.18	0.15	0.16	461
19	(0, 7]	(-0.05, 0.0]	-1	7.62	0.64	0.37	1.03	505
20	(0, 7]	(0.0, 0.05]	-1	68	2.34	-0.89	8.65	269
21	(0, 7]	(0.05, 0.1]	-1	-1	-1	-1	0	2
22	(7, 14]	(-1.0, -0.95]	0.01	0.09	0.05	0.04	0.03	12
23	(7, 14]	(-0.95, -0.9]	0.01	0.1	0.05	0.05	0.02	22
24	(7, 14]	(-0.9, -0.85]	0.01	0.09	0.05	0.05	0.02	16
25	(7, 14]	(-0.85, -0.8]	0.01	0.11	0.06	0.06	0.03	20
26	(7, 14]	(-0.8, -0.75]	0.01	0.11	0.06	0.06	0.03	25
27	(7, 14]	(-0.75, -0.7]	0.01	0.12	0.06	0.06	0.03	21
28	(7, 14]	(-0.7, -0.65]	0.02	0.12	0.07	0.06	0.02	33
29	(7, 14]	(-0.65, -0.6]	0.02	0.13	0.07	0.07	0.03	27
30	(7, 14]	(-0.6, -0.55]	0.02	0.14	0.07	0.07	0.03	45
31	(7, 14]	(-0.55, -0.5]	0.02	0.15	0.07	0.07	0.03	114
32	(7, 14]	(-0.5, -0.45]	0.02	0.16	0.08	0.08	0.03	153
33	(7, 14]	(-0.45, -0.4]	0.02	0.17	0.09	0.08	0.04	166
34	(7, 14]	(-0.4, -0.35]	0.02	0.19	0.09	0.09	0.04	197
35	(7, 14]	(-0.35, -0.3]	0.02	0.21	0.11	0.1	0.04	235
36	(7, 14]	(-0.3, -0.25]	0.03	0.25	0.13	0.12	0.05	265
37	(7, 14]	(-0.25, -0.2]	0.03	0.3	0.15	0.14	0.06	280
38	(7, 14]	(-0.2, -0.15]	0.04	0.38	0.18	0.18	0.08	307
39	(7, 14]	(-0.15, -0.1]	0.05	0.54	0.25	0.24	0.11	332
40	(7, 14]	(-0.1, -0.05]	0.07	0.97	0.4	0.39	0.18	370
41	(7, 14]	(-0.05, 0.0]	-0.46	4.4	1.02	0.86	0.68	404
42	(7, 14]	(0.0, 0.05]	-1	32	1.52	-0.73	4.45	388
43	(7, 14]	(0.05, 0.1]	-1	-0.83	-0.93	-0.94	0.06	36

There are more customization options for Optopsy's strategy functions, consult the codebase/future documentation to see how it can be used to adjust the results, such as increasing/decreasing the intervals and other data to be returned.

The library is currently under development, as such expect changes to the API in the future.